Title

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data

Authors

Jaiprasath Sachithanandham1, Karnati Konda Reddy2, King Solomon1, Shoba David1, Sanjeev
Kumar Singh2, Veena Vadhini Ramalingam1, Susanne Alexander Pulimood3, Ooriyapadickal
Cherian Abraham4, Pricilla Rupali4, Gopalan Sridharan5 & Rajesh Kannangai1*

Affiliation

Departments of Clinical Virology1, Dermatology3, Internal Medicine4, Christian Medical College, Vellore, Sri Sakthi Amma Institute of Biomedical Research Institute5, SNHRC Vellore and Computer-Aided Drug Design and Molecular Modeling Lab2, Department of Bioinformatics2, Alagappa University, Karaikudi, Tamil Nadu, India.

Email

Rajesh Kannangai– Email: kannangair@cmcvellore.ac.in;
Phone: 91 (416) 2282070; Fax #; 91 (416) 2232035; *Corresponding author

Article Type

Hypothesis

Date

Received February 4, 2016; Revised February 29, 2016; Accepted March 2, 2016; Published June 15, 2016

The degree of sequence variation in HIV-1 integrase genes among infected patients and their impact on clinical response to Anti retroviral therapy (ART) is of interest. Therefore, we collected plasma samples from 161 HIV-1 infected individuals for subsequent integrase gene amplification (1087 bp). Thus, 102 complete integrase gene sequences identified as HIV-1 subtype-C was assembled. This sequence data was further used for sequence analysis and multiple sequence alignment (MSA) to assess position specific frequency of mutations within pol gene among infected individuals. We also used biophysical geometric optimization technique based molecular modeling and docking (Schrodinger suite) methods to infer differential function caused by position specific
sequence mutations towards improved inhibitor selection. We thus identified accessory mutations (usually reduce susceptibility) leading to the resistance of some known integrase inhibitors in 14% of sequences in this data set. The Stanford HIV-1 drug resistance database provided complementary information on integrase resistance mutations to deduce molecular basis for such
observation. Modeling and docking analysis show reduced binding by mutants for known compounds. The predicted binding values further reduced for models with combination of mutations among subtype C clinical strains. Thus, the molecular basis implied for the consequence of mutations in different variants of integrase genes of HIV-1 subtype C clinical strains from South
India is reported. This data finds utility in the design, modification and development of a representative yet an improved inhibitor for HIV-1 integrase.

Citation

Sachithanandham et al. Bioinformation 12(3): 221-230 (2016)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.